全球工業化迅速發展，導致能源生產短缺。由於工業化，能源和消費水平高於預期，並在台灣和世界範圍內發揮重要作用。隨著環境和能源危機帶來的嚴重威脅，改善和發展可再生能源作為傳統化石能源替代品的決心有所增強。其次，作為增加使用可再生能源以取代燃煤能源的關鍵組成部分，能源部門一直在得到改善。日前，高比例的能源消耗來自於作為能源來源的化石燃料。在這方面，太陽能是當今最有前途的可再生能源之。台灣的《可再生能源發展法案》是一項計劃，旨在在全國范圍內鼓勵綠色技術，並引領工業革命，到2025年將可再生能源的20％轉化為太陽能的三分之二。本論文旨在評估台灣地區九座太陽能光電發電廠在2018年至2020年期間之績效。此外，本論文亦以衛星數據及發電量預測全球水平輻射（GHI），以評估台灣地區九家太陽能光電發電站在2021-2025年間的未來表現。本文采用灰色模型（GM（1,1））和數據包絡分析（DEA）的Epsilon基測度EBM模型分別對9家太陽能光電電站的績效進行預測和評價。
本文采用EBM模型對選取的9家太陽能光電電站在2018年至2020年的績效進行評價。然後，利用衛星數據中2年的全球水平輻照歷史數據和所選太陽能光電電站3年的歷史數據，利用GM（1,1）預測全球水平輻照和發電量。此外，衛星數據和產生的能量的全球水平輻照將與實際表面積和模塊數量相結合，使用EBM模型預測和評估9個太陽能光電電站在2021年至2025年的未來性能。
本論文之研究結果顯示，循證醫學模型及GM（1,1）模型可作為評估台灣九家太陽能光電發電廠過往及未來績效的有效工具。而太陽能光電電站的發電量下降幅度大於0.5％本文雖然成功地對太陽能光電電站的性能進行了評價，但仍存在一些局限性，需要進一步研究。由於數據有限，只關注宏觀層面的分析，也可以增加微觀層面的分析，更好的分析太陽能光電發電廠。在未來的研究中，GM（1,1）和EBM模型可以應用到其他國家或地區，進一步分析模型的適用性，因為預測和評價的結果會因地區而異。因此，本文的研究將有助於研究人員，電廠業主和運營管理者對太陽能光電電站的運行維護管理和發展提供幫助。本研究將指導決策，以維持和發展現有的太陽能光電發電廠。在不久的將來，本研究亦將GM（1,1）及EBM模型推廣至菲律賓，因台灣在景觀，環境，氣候及太陽輻照等方面存在差異，並將物聯網及機器學習應用於評估太陽能光電發電廠的效能。

Global industrialization is expanding quickly, leading to energy production shortages. The levels of energy and consumption were higher than expected and play a major role in Taiwan and worldwide, owing to industrialization. Increased determination to the improvement and growth of renewables as an alternative to conventional fossil energy has grown alongside the severe threats posed by environmental and energy crises. Secondly, as a key component to increasing renewable energy use to replace coal-fired energies, the energy sector is always being improved. The high percentage of energy consumption currently is coming from fossil fuels as a source of energy. In this regard, one of the promising renewable sources of energy nowadays comes from solar energy. Taiwan’s renewable development act is a plan to encourage green technology all over the country and spearhead the industrial revolution to established two-thirds of solar energy from 20% of renewable energy by 2025. This dissertation aims to evaluate the performance of nine solar PV power plants selected around Taiwan from 2018 to 2020. Furthermore, the dissertation also forecasts global horizontal irradiation (GHI) using satellite-derived data and generated energy to evaluate the future performance of nine solar PV power plants around Taiwan from 2021 to 2025. The dissertation used the Grey model (GM (1,1)) and Epsilon base measure (EBM) model of Data Envelopment Analysis (DEA) to forecast and evaluate the performance of nine solar PV power plants, respectively.
The method in this dissertation was used to evaluate the performance of nine selected solar PV power plants from 2018 to 2020 using the EBM model. Then, using GM (1,1) to forecast the global horizontal irradiation and generated energy using two years of historical global horizontal irradiation from satellite-derived data and three years of historical data of the selected solar PV power plant. Moreover, global horizontal irradiation from the satellite-derived data and generated energy will be combined with the actual surface area and the number of modules to forecast and evaluate the future performance of nine solar PV power plants from 2021 to 2025 using the EBM model.
The result of the dissertation shows that EBM and GM (1, 1) models were a useful tool to evaluate the previous and future performance of the nine solar PV power plants in Taiwan. However, the decrease of generated energy of the solar PV power plants was more than 0.5 %. Although this dissertation is successful in evaluating the performance of the solar PV power plant some limitations still exist and need further study. Since the data are limited and only focus on the macro-level of analysis, analysis from the micro-level can also be added for better analysis of the solar PV power plant. For the future study, the GM (1,1) and EBM models can be applied to other countries or locations to further analyze the applicability of the models, since the results of the forecasting and evaluation vary on the location. Therefore, this dissertation will help the researchers, plant owners, and operations managers with the operation and maintenance management as well as the development of the solar PV power plant. This study will lead as a guide for decision-making to sustain and develop an existing solar PV power plant. In the near future, the study will also extend the GM (1,1) and EBM models to the Philippines, since Taiwan differs in the landscape, environment, climate, and solar irradiation to evaluate the performance of solar PV power plants with the aid of the internet of things and machine learning.

Approval Sheet i
摘 要 ii
ABSTRACT iv
ACKNOWLEDGEMENT vi
LIST OF TABLES x
LIST OF FIGURES xiii
NOMENCLATURE xiv
Chapter 1 1
1-1 Research background 1
1-2 Motivation and Importance of the research 4
1-3 Research framework 7
Chapter 2 8
2-1 Literature review of Data Envelopment analysis 8
2-1-1 Epsilon based measurement model 11
2-2 Related literature on solar PV power plant performance evaluation 11
2-3 Literature review of Forecasting models 12
2-3-1 Grey model GM (1,1) 13
2-4 Related literature on solar irradiation forecasting and energy forecasting 14
Chapter 3 17
3-1 Research process 17
3-2 Data Gathering 18
3-2 Epsilon-based measure model 21
3-2-1 Epsilon and weights determination 22
3.3 Grey Model 26
3-3-1 Evaluation Parameter 28
3-3-2 Parameter for MAPE 29
Chapter 4 30
4.1 Forecasting using GM (1,1) model 30
4.2 Epsilon Base Measurement (EBM) model results 36
Chapter 5 45
Appendix A 47
REFERENCES 66
VITA 73

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